Multi-domain encoder–decoder neural networks for latent data assimilation in dynamical systems

High-dimensional dynamical systems often require computationally intensive physics-based simulations, making full physical space data assimilation impractical. Latent data assimilation methods perform assimilation in reduced-order latent space for efficiency but struggle with complex, nonlinear stat...

Celý popis

Podrobná bibliografia

Vydané v:	Computer methods in applied mechanics and engineering Ročník 430; s. 117201
Hlavní autori:	Cheng, Sibo, Zhuang, Yilin, Kahouadji, Lyes, Liu, Che, Chen, Jianhua, Matar, Omar K., Arcucci, Rossella
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.10.2024 Elsevier
Predmet:	Computer Science Data assimilation Data fusion Deep learning Dynamical systems Environmental Sciences Nonlinear Sciences Physics Deep learning Data fusion Dynamical systems Data assimilation Data assimilation Deep learning Data fusion Dynamical systems
ISSN:	0045-7825, 1879-2138
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!